Announced in 2016, Gym is an open-source Python library created to facilitate the advancement of support knowing algorithms. It aimed to standardize how environments are defined in AI research study, making released research more easily reproducible [24] [144] while offering users with a basic interface for connecting with these environments. In 2022, new developments of Gym have actually been transferred to the library Gymnasium. [145] [146]
Gym Retro

Released in 2018, Gym Retro is a platform for support knowing (RL) research on computer game [147] utilizing RL algorithms and research study generalization. Prior RL research focused mainly on optimizing agents to fix single tasks. Gym Retro provides the ability to generalize between video games with similar principles but various appearances.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents at first do not have knowledge of how to even stroll, but are given the goals of finding out to move and to push the opposing agent out of the ring. [148] Through this adversarial learning process, the representatives learn how to adapt to altering conditions. When an agent is then eliminated from this virtual environment and put in a new virtual environment with high winds, the agent braces to remain upright, recommending it had actually discovered how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition between representatives could create an intelligence "arms race" that could increase a representative's ability to work even outside the context of the competitors. [148]
OpenAI 5

OpenAI Five is a group of five OpenAI-curated bots used in the competitive five-on-five computer game Dota 2, higgledy-piggledy.xyz that discover to play against human players at a high skill level entirely through experimental algorithms. Before ending up being a group of 5, the very first public demonstration took place at The International 2017, the yearly premiere champion competition for the game, where Dendi, an expert Ukrainian player, lost against a bot in a live individually matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually discovered by playing against itself for two weeks of actual time, which the learning software application was an action in the direction of producing software that can manage intricate jobs like a surgeon. [152] [153] The system utilizes a type of reinforcement learning, as the bots learn over time by playing against themselves numerous times a day for months, and are rewarded for actions such as eliminating an opponent and taking map goals. [154] [155] [156]
By June 2018, the capability of the bots broadened to play together as a full team of 5, and they were able to beat teams of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibit matches against expert players, however wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the reigning world champions of the video game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' last public appearance came later on that month, where they played in 42,729 overall games in a four-day open online competition, winning 99.4% of those video games. [165]
OpenAI 5's systems in Dota 2's bot gamer reveals the challenges of AI systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has shown the usage of deep support knowing (DRL) agents to attain superhuman skills in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes maker learning to train a Shadow Hand, a human-like robot hand, to manipulate physical items. [167] It learns totally in simulation utilizing the very same RL algorithms and training code as OpenAI Five. OpenAI took on the things orientation issue by utilizing domain randomization, a simulation method which exposes the student to a variety of experiences rather than attempting to fit to truth. The set-up for Dactyl, aside from having movement tracking video cameras, likewise has RGB cameras to permit the robot to manipulate an approximate things by seeing it. In 2018, OpenAI showed that the system was able to manipulate a cube and an octagonal prism. [168]
In 2019, OpenAI showed that Dactyl might fix a Rubik's Cube. The robotic was able to solve the puzzle 60% of the time. Objects like the Rubik's Cube present complicated physics that is harder to model. OpenAI did this by improving the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of generating progressively harder environments. ADR varies from manual domain randomization by not requiring a human to define randomization varieties. [169]
API

In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing brand-new AI designs developed by OpenAI" to let designers contact it for "any English language AI job". [170] [171]
Text generation

The business has actually popularized generative pretrained transformers (GPT). [172]
OpenAI's original GPT design ("GPT-1")

The original paper on generative pre-training of a transformer-based language design was written by Alec Radford and his colleagues, fishtanklive.wiki and released in preprint on OpenAI's site on June 11, 2018. [173] It demonstrated how a generative model of language could obtain world knowledge and procedure long-range reliances by pre-training on a varied corpus with long stretches of contiguous text.

GPT-2

Generative Pre-trained Transformer 2 ("GPT-2") is a not being watched transformer language design and the follower to OpenAI's original GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with only restricted demonstrative variations initially released to the public. The complete variation of GPT-2 was not immediately released due to concern about prospective misuse, consisting of applications for writing fake news. [174] Some experts revealed uncertainty that GPT-2 presented a significant risk.

In action to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to identify "neural phony news". [175] Other researchers, such as Jeremy Howard, warned of "the innovation to completely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would drown out all other speech and be difficult to filter". [176] In November 2019, OpenAI released the total version of the GPT-2 language model. [177] Several sites host interactive demonstrations of different instances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2's authors argue unsupervised language designs to be general-purpose learners, shown by GPT-2 attaining advanced accuracy and perplexity on 7 of 8 zero-shot jobs (i.e. the model was not more trained on any task-specific input-output examples).

The corpus it was trained on, called WebText, contains a little 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 upvotes. It prevents certain problems encoding vocabulary with word tokens by using byte pair encoding. This permits representing any string of characters by encoding both private characters and multiple-character tokens. [181]
GPT-3

First explained in May 2020, Generative Pre-trained [a] Transformer 3 (GPT-3) is an unsupervised transformer language design and the successor to GPT-2. [182] [183] [184] OpenAI specified that the complete version of GPT-3 contained 175 billion specifications, [184] 2 orders of magnitude larger than the 1.5 billion [185] in the full variation of GPT-2 (although GPT-3 models with as few as 125 million criteria were likewise trained). [186]
OpenAI mentioned that GPT-3 succeeded at certain "meta-learning" tasks and might generalize the function of a single input-output pair. The GPT-3 release paper offered examples of translation and cross-linguistic transfer learning in between English and Romanian, and between English and German. [184]
GPT-3 considerably enhanced benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language models could be approaching or experiencing the fundamental ability constraints of predictive language designs. [187] Pre-training GPT-3 required numerous thousand petaflop/s-days [b] of compute, compared to 10s of petaflop/s-days for the complete GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained model was not immediately launched to the public for issues of possible abuse, although OpenAI prepared to allow gain access to through a paid cloud API after a two-month free private beta that started in June 2020. [170] [189]
On September 23, 2020, GPT-3 was licensed exclusively to Microsoft. [190] [191]
Codex

Announced in mid-2021, Codex is a descendant of GPT-3 that has actually furthermore been trained on code from 54 million GitHub repositories, [192] [193] and is the AI powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was released in personal beta. [194] According to OpenAI, the model can create working code in over a dozen shows languages, most effectively in Python. [192]
Several problems with glitches, design defects and security vulnerabilities were pointed out. [195] [196]
GitHub Copilot has been accused of emitting copyrighted code, without any author attribution or license. [197]
OpenAI announced that they would cease support for wiki.whenparked.com Codex API on March 23, 2023. [198]
GPT-4

On March 14, 2023, OpenAI announced the release of Generative Pre-trained Transformer 4 (GPT-4), efficient in accepting text or image inputs. [199] They revealed that the updated innovation passed a simulated law school bar exam with a rating around the top 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could likewise check out, analyze or generate approximately 25,000 words of text, and write code in all major programs languages. [200]
Observers reported that the version of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based iteration, with the caveat that GPT-4 retained a few of the issues with earlier revisions. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has declined to expose various technical details and stats about GPT-4, such as the precise size of the model. [203]
GPT-4o

On May 13, 2024, OpenAI announced and released GPT-4o, which can process and generate text, images and audio. [204] GPT-4o attained state-of-the-art lead to voice, multilingual, and vision benchmarks, setting new records in audio speech recognition and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) standard compared to 86.5% by GPT-4. [207]
On July 18, 2024, higgledy-piggledy.xyz OpenAI launched GPT-4o mini, it-viking.ch a smaller sized version of GPT-4o changing GPT-3.5 Turbo on the ChatGPT user interface. Its API costs $0.15 per million input tokens and $0.60 per million output tokens, compared to $5 and $15 respectively for GPT-4o. OpenAI expects it to be particularly beneficial for business, start-ups and designers looking for to automate services with AI agents. [208]
o1

On September 12, 2024, OpenAI released the o1-preview and o1-mini models, which have actually been developed to take more time to consider their reactions, leading to greater accuracy. These designs are especially effective in science, coding, and thinking tasks, and were made available to ChatGPT Plus and Staff member. [209] [210] In December 2024, o1-preview was replaced by o1. [211]
o3

On December 20, 2024, OpenAI revealed o3, raovatonline.org the follower of the o1 reasoning model. OpenAI likewise unveiled o3-mini, a lighter and faster variation of OpenAI o3. As of December 21, 2024, this design is not available for public use. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security scientists had the opportunity to obtain early access to these models. [214] The design is called o3 instead of o2 to prevent confusion with telecoms providers O2. [215]
Deep research study

Deep research is an agent developed by OpenAI, revealed on February 2, 2025. It leverages the abilities of OpenAI's o3 model to carry out substantial web surfing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to 30 minutes. [216] With searching and Python tools allowed, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) criteria. [120]
Image category

CLIP

Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a model that is trained to analyze the semantic similarity between text and images. It can notably be used for image classification. [217]
Text-to-image

DALL-E

Revealed in 2021, DALL-E is a Transformer design that produces images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter variation of GPT-3 to translate natural language inputs (such as "a green leather purse shaped like a pentagon" or "an isometric view of an unfortunate capybara") and create matching images. It can develop pictures of reasonable items ("a stained-glass window with an image of a blue strawberry") along with objects that do not exist in reality ("a cube with the texture of a porcupine"). Since March 2021, no API or code is available.

DALL-E 2

In April 2022, OpenAI announced DALL-E 2, an updated variation of the design with more sensible outcomes. [219] In December 2022, OpenAI released on GitHub software for Point-E, setiathome.berkeley.edu a new simple system for converting a text description into a 3-dimensional model. [220]
DALL-E 3

In September 2023, OpenAI announced DALL-E 3, a more effective design much better able to generate images from without manual timely engineering and render intricate details like hands and text. [221] It was released to the public as a ChatGPT Plus function in October. [222]
Text-to-video

Sora

Sora is a text-to-video design that can produce videos based upon brief detailed prompts [223] as well as extend existing videos forwards or in reverse in time. [224] It can generate videos with resolution up to 1920x1080 or 1080x1920. The maximal length of produced videos is unidentified.

Sora's advancement group called it after the Japanese word for "sky", to symbolize its "limitless innovative capacity". [223] Sora's innovation is an adaptation of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos as well as copyrighted videos accredited for that purpose, but did not reveal the number or the precise sources of the videos. [223]
OpenAI demonstrated some Sora-created high-definition videos to the general public on February 15, 2024, specifying that it could create videos up to one minute long. It also shared a technical report highlighting the approaches used to train the design, and the model's capabilities. [225] It acknowledged some of its shortcomings, consisting of struggles mimicing complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "remarkable", however kept in mind that they must have been cherry-picked and might not represent Sora's normal output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demo, notable entertainment-industry figures have revealed considerable interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the innovation's capability to generate realistic video from text descriptions, mentioning its potential to transform storytelling and content development. He said that his enjoyment about Sora's possibilities was so strong that he had actually decided to stop briefly prepare for expanding his Atlanta-based film studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech recognition model. [228] It is trained on a large dataset of diverse audio and is also a multi-task model that can carry out multilingual speech recognition in addition to speech translation and language identification. [229]
Music generation

MuseNet

Released in 2019, MuseNet is a deep neural net trained to predict subsequent musical notes in MIDI music files. It can create tunes with 10 instruments in 15 designs. According to The Verge, a tune created by MuseNet tends to begin fairly however then fall into chaos the longer it plays. [230] [231] In pop culture, preliminary applications of this tool were utilized as early as 2020 for the web psychological thriller Ben Drowned to produce music for the titular character. [232] [233]
Jukebox

Released in 2020, Jukebox is an open-sourced algorithm to create music with vocals. After training on 1.2 million samples, the system accepts a genre, artist, and a snippet of lyrics and outputs song samples. OpenAI stated the songs "reveal regional musical coherence [and] follow standard chord patterns" however acknowledged that the songs lack "familiar bigger musical structures such as choruses that repeat" which "there is a substantial gap" between Jukebox and human-generated music. The Verge specified "It's technologically impressive, even if the outcomes seem like mushy versions of songs that might feel familiar", while Business Insider mentioned "remarkably, a few of the resulting songs are memorable and sound genuine". [234] [235] [236]
Interface

Debate Game

In 2018, OpenAI launched the Debate Game, which teaches machines to dispute toy issues in front of a human judge. The function is to research whether such an approach might assist in auditing AI decisions and in establishing explainable AI. [237] [238]
Microscope

Released in 2020, Microscope [239] is a collection of visualizations of every significant layer and neuron of 8 neural network designs which are typically studied in interpretability. [240] Microscope was developed to evaluate the functions that form inside these neural networks easily. The models included are AlexNet, VGG-19, different variations of Inception, and different versions of CLIP Resnet. [241]
ChatGPT

Launched in November 2022, ChatGPT is a synthetic intelligence tool constructed on top of GPT-3 that supplies a conversational interface that enables users to ask questions in natural language. The system then responds with an answer within seconds.